vLLM: High-Performance LLM Serving

from vllm import LLM, SamplingParams
import asyncio
from typing import List, Dict, Any

class vLLMServer:
    """High-performance LLM serving with vLLM"""
    
    def __init__(self, model_name: str, tensor_parallel_size: int = 1):
        self.model_name = model_name
        self.llm = LLM(
            model=model_name,
            tensor_parallel_size=tensor_parallel_size,
            dtype="float16",
            gpu_memory_utilization=0.9,
            max_num_seqs=256,
            max_model_len=4096
        )
        
    def generate(self, prompts: List[str], **kwargs) -> List[str]:
        """Generate responses for multiple prompts"""
        sampling_params = SamplingParams(
            temperature=kwargs.get('temperature', 0.7),
            top_p=kwargs.get('top_p', 0.9),
            max_tokens=kwargs.get('max_tokens', 512)
        )
        
        outputs = self.llm.generate(prompts, sampling_params)
        return [output.outputs[0].text for output in outputs]
    
    async def stream_generate(self, prompt: str, **kwargs):
        """Stream generation for real-time responses"""
        sampling_params = SamplingParams(
            temperature=kwargs.get('temperature', 0.7),
            max_tokens=kwargs.get('max_tokens', 512),
            stream=True
        )
        
        results = self.llm.generate([prompt], sampling_params)
        async for output in results:
            yield output.outputs[0].text

# Example usage
def deploy_llama_model():
    # Initialize vLLM server
    server = vLLMServer("meta-llama/Llama-2-7b-chat-hf", tensor_parallel_size=2)
    
    # Batch generation
    prompts = [
        "Explain quantum computing in simple terms:",
        "Write a Python function for binary search:",
        "What are the benefits of renewable energy?"
    ]
    
    responses = server.generate(prompts, temperature=0.8, max_tokens=256)
    for prompt, response in zip(prompts, responses):
        print(f"Prompt: {prompt}")
        print(f"Response: {response}\n")
    
    return server